Acta Informatica Pragensia

Said Al Afghani Edsa

Khamron Sunat

Induced Partitioning for Incremental Feature Selection via Rough Set Theory and Long-tail Position Grey Wolf Optimizer

基于粗糙集理论和长尾位置灰狼优化器的增量特征选择诱导分区

Insaf Boumezbeur

Abdelhalim Benoughidene

Imane Harkat

Farah Boutouatou

Dounia Keddari

Karim Zarour

BACP-LRS: Blockchain and IPFS-based Land Record System

BACP-LRS：基于区块链和IPFS的土地记录系统

Kemal Elciyar

Neşe Satılmış

Development and Validation of a Blockchain Literacy Scale

区块链素养量表的开发与验证

Over the past few decades, the enormous expansion of medical data has led to searching for ways of data analysis in smart healthcare systems.Acquisition of data from pictures, archives, communication systems, electronic health records, online documents, radiology reports and clinical records of different styles with specific numerical information has given rise to the concept of multimodality and the need for machine learning and deep learning techniques in the analysis of the healthcare system.Medical data play a vital role in medical education and diagnosis; determining dependency between distinct modalities is essential.This paper gives a gist of current radiology medical data analysis techniques and their various approaches and frameworks for representation and classification.A brief outline of the existing medical multimodal data processing work is presented.The main objective of this study is to spot gaps in the surveyed area and list future tasks and challenges in radiology.The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (or PRISMA) guidelines were incorporated in this study for effective article search and to investigate several relevant scientific publications.The systematic review was carried out on multimodal medical data analysis and highlighted advantages, limitations and strategies.The inherent benefit of multimodality in the medical domain powered with artificial intelligence has a significant impact on the performance of the disease diagnosis frameworks.

Shashank Shetty

Ananthanarayana V S

Ajit Mahale

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

多模态医学数据分析综合评述：开放问题与未来研究方向

In recent years, community detection is important because members of the same community share the same concepts.For efficient community detection in a social network, the influence node plays a vital role.A node in the social network or a user that has great influence and power would have a close relationship with a core of the group, termed a community.Therefore, the status of a person is determined by the user's influence strength.That is, a user who has greater influence and strength plays a vital role in the social media community and also acts as a core in the community of the social network.Therefore, a community is a group of nodes in the complex network structure which are interlinked with one another.Effective community detection in a complex structure is a challenging task.Many studies have been done based on topological networks.The approaches are ineffective, inefficient and require more time to process.To overcome these issues, this paper proposes improving the influence nodes in complex networks by using the InfoMap with sigmoid fish swarm optimization algorithm (I-SFSO).Our proposed I-SFSO gives better accuracy rates for the data sets: 92% for Dolphin, 95% for the Facebook dataset, 96% for the Twitter data set, 94% for the YouTube data set, 93% for a karate club and 94% for football.

Devi Selvaraj

Rajalakshmi Murugasamy

Evaluation of Community Detection by Improving Influence Nodes in Complex Networks Using InfoMap with Sigmoid Fish Swarm Optimization Algorithm

基于Sigmoid鱼群优化算法的InfoMap改进复杂网络中影响力节点社区检测评估

Rachid Beghdad

Faiza Benmenzer

Alaa Eddine Khalfoune

Cloud Survivability Scenarios Under Attacks With and Without Countermeasures

云环境下攻击有/无对抗措施的生存性场景

The integration of generative artificial intelligence (AI) into adaptive and personalized learning represents a transformative shift in the educational landscape.This research paper investigates the impact of incorporating generative AI into adaptive and personalized learning environments, with a focus on tracing the evolution from conventional artificial intelligence methods to generative AI and identifying its diverse applications in education.The study begins with a comprehensive review of the evolution of generative AI models and frameworks.A framework of selection criteria is established to curate case studies showcasing the applications of generative AI in education.These case studies are analysed to elucidate the benefits and challenges associated with integrating generative AI into adaptive learning frameworks.Through an in-depth analysis of selected case studies, the study reveals tangible benefits derived from generative AI integration, including increased student engagement, improved test scores and accelerated skill development.Ethical, technical and pedagogical challenges related to generative AI integration are identified, emphasizing the need for careful consideration and collaborative efforts between educators and technologists.The findings underscore the transformative potential of generative AI in revolutionizing education.By addressing ethical concerns, navigating technical challenges and embracing human-centric approaches, educators and technologists can collaboratively harness the power of generative AI to create innovative and inclusive learning environments.Additionally, the study highlights the transition from Education 4.0 to Education 5.0, emphasizing the importance of social-emotional learning and human connection alongside personalization in shaping the future of education.

Manel Guettala

Samir Bourekkache

Okba Kazar

Saad Harous

Generative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning

生成式人工智能在教育中的应用：推进自适应与个性化学习

Abbreviation
Journal Impact	0.79
Quartiles(Global)	SOCIAL SCIENCES, INTERDISCIPLINARY(Q3)

Publication Information	Publisher: Prague University of Economics and Business，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2019，Proportion of original research papers: 0.00%，Self Citation Rate:0.00%， Gold OA Rate: 98.59%
Average review cycle	网友分享经验：12 Weeks

Acta Informatica Pragensia

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Induced Partitioning for Incremental Feature Selection via Rough Set Theory and Long-tail Position Grey Wolf Optimizer

BACP-LRS: Blockchain and IPFS-based Land Record System

Development and Validation of a Blockchain Literacy Scale

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

Evaluation of Community Detection by Improving Influence Nodes in Complex Networks Using InfoMap with Sigmoid Fish Swarm Optimization Algorithm

Induced Partitioning for Incremental Feature Selection via Rough Set Theory and Long-tail Position Grey Wolf Optimizer

BACP-LRS: Blockchain and IPFS-based Land Record System

Development and Validation of a Blockchain Literacy Scale

Cloud Survivability Scenarios Under Attacks With and Without Countermeasures

Generative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning

ISSN	1805-4951
h-index	6